• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.16-24
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• 2002

Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform. The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.

• Journal of Biosystems Engineering
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• v.42 no.2
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• pp.117-125
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• 2017

Purpose: Damage to pulse crops by wild birds is a serious problem. The damage is to such an extent that the rate of damage during the period between seeding and cotyledon stages reaches 54.6% on an average. In this study, a crop-position detection method was developed wherein infrared (IR) sensors were used to determine the cotyledon position under a vinyl mulch. Methods: IR sensors that helped measure the temperature were used to locate the cotyledons below the vinyl mulch. A single IR sensor module was installed at three locations of the crops (peanut, red lettuce, and crown daisy) in the cotyledon stage. The representative thermal response of a $16{\times}4$ pixel area was detected using this sensor in the case where the distance from the target was 25 cm. A spatial image was applied to the two-dimensional temperature distribution using a non-integral moving-average method. The collected data were first processed by taking the moving average via interpolation to determine the frame where the variance was the lowest for a resolution unit of 1.02 cm. Results: The temperature distribution was plotted corresponding to a distance of 10 cm between the crops. A clear leaf pattern of the crop was visually confirmed. However, the temperature distribution after the normalization was unclear. The image conversion and frequency-conversion graphs were obtained based on the moving average by averaging the points corresponding to a frequency of 40 Hz for 8 pixels. The most optimized resolutions at locations 1, 2, and 3 were found on 3.4, 4.1, and 5.6 Pixels, respectively. Conclusions: In this study, to solve the problem of damage caused by birds to crops in the cotyledon stage after seeding, the vinyl mulch is punched after seeding. The crops in the cotyledon stage could be accurately located using the proposed method. By conducting the experiments using the single IR sensor and a sliding mechanical device with the help of a non-integral interpolation method, the crops in the cotyledon stage could be precisely located.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.9-21
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• 1995

A planning support system enhances our ability to use water capacity expansion as an urban growth management strategy. This paper reports the development of capacity expansion modeling of water supply as part of the continuing development of such a planning support system (PEGASUS: Planning Environment for Generation and Analysis of Spatial Urban Systems) to incorporate water supply, This system is designed from the understanding that land use and development drive the demand for infrastructure and infrastructure can have a significant influence on the ways in which land is developed and used. Capacity expansion Problems of water supply can be solved in two ways: 1) optimal control theory, and 2) mixed integer nonlinear programming (MINLP). Each method has its strengths and weaknesses. In this study the MINLP approach is used because of its strength of determining expansion sizing and timing simultaneously. A dynamic network optimization model and a water-distribution network analysis model can address the dynamic interdependence between water planning and land use planning. While the water-distribution network analysis model evaluates the performance of generated networks over time, the dynamic optimization model chooses alternatives to meet expanding water needs. In addition, the user and capacity expansion modeling-to-generate-alternatives (MGA) can generate alternatives. A cost benefit analysis module using a normalization technique helps in choosing the most economical among those alternatives. GIS provide a tool for estimating the volume of demanded water and showing results of the capacity expansion model.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.114-125
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• 2023

The physics-informed neural network (PINN) has been proposed to overcome the limitations of various numerical methods used to solve partial differential equations (PDEs) and the drawbacks of purely data-driven machine learning. The PINN directly applies PDEs to the construction of the loss function, introducing physical constraints to machine learning training. This technique can also be applied to wave equation modeling. However, to solve the wave equation using the PINN, second-order differentiations with respect to input data must be performed during neural network training, and the resulting wavefields contain complex dynamical phenomena, requiring careful strategies. This tutorial elucidates the fundamental concepts of the PINN and discusses considerations for wave equation modeling using the PINN approach. These considerations include spatial coordinate normalization, the selection of activation functions, and strategies for incorporating physics loss. Our experimental results demonstrated that normalizing the spatial coordinates of the training data leads to a more accurate reflection of initial conditions in neural network training for wave equation modeling. Furthermore, the characteristics of various functions were compared to select an appropriate activation function for wavefield prediction using neural networks. These comparisons focused on their differentiation with respect to input data and their convergence properties. Finally, the results of two scenarios for incorporating physics loss into the loss function during neural network training were compared. Through numerical experiments, a curriculum-based learning strategy, applying physics loss after the initial training steps, was more effective than utilizing physics loss from the early training steps. In addition, the effectiveness of the PINN technique was confirmed by comparing these results with those of training without any use of physics loss.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1559-1572
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• 2021

The response of vegetation under the crop stress condition was evaluated using structural, biochemical, and physiological vegetation indices based on unmanned aerial vehicle (UAV) images and field-spectrometer data. A high concentration of herbicide was sprayed at the different growth stages of garlic to process crop stress, the above ground dry matter of garlic at experimental area (EA) decreased about 46.2~84.5% compared to that at control area. The structural vegetation indices clearly responded to these crop damages. Spectral reflectance at near-infrared wavelength consistently decreased at EA. Most biochemical vegetation indices reflected the crop stress conditions, but the meaning of physiological vegetation indices is not clear due to the effect of vinyl mulching. The difference of the decreasing ratio of vegetation indices after the herbicide spray was 2.3% averagely in the case of structural vegetation indices and 1.3~4.1% in the case of normalization-based vegetation indices. These results meant that appropriate vegetation indices should be utilized depending on the types of crop stress and the cultivation environment and the normalization-based vegetation indices measured from the different spatial scale has the minimized difference.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.2 s.12
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• pp.105-117
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• 1998

The main object of this study was to prove the effectiveness of satellite Image data for extraction of the pine needle gall midge damaged area in the part of Kangwon-do area, and to present the detailed procedure of a digital image processing for extraction of those damaged area. The effectiveness of extraction of damaged area was improved by using the BRCT(Backwards Radiance Correction Transformation) with DEM for the normalization of topographic effects. The topographic surface analysis of the extracted damaged area revealed that the general damaged area was at south-west and south-east aspect with the slope of 31 to 38 degrees, the temperature of 21 to 25, and 23% to 39% of the highest altitude mountains. The new damaged area in which expanded area was at 27 to 30 degree of slope, the aspect of 46 to 180 degrees, the temperature of $11^{\circ}C\;to\;12^{\circ}C$ and 27% to 39% of the highest altitude mountains. The NDI(New Damaged Index) was developed using the environment factor and simple vegetation index.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.4 s.27
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• pp.21-27
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• 2003

The rational polynomial coefficients(RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS-2 and QuickBird. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective camera the linear pushbroom sensor and the SAR This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects $510{\sim}730nm$ panchromatic images with a ground sample distance (GSD) of 6.6m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.367-380
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• 2013

In this study, sub-indicators, and thematic mid-indexes to evaluate the water use characteristics were selected through historical data analysis and factor analysis, and consisted of the subject approach framework. And the integrated index was developed to evaluate water use characteristics of the watershed. Using developed index, the water use characteristics were assessed for 812 standard basins with the exception for North Korea using data of 1990 to 2007 from the relevant agencies. A sensitivity analysis is conducted for this study to determine the proper way through various normalization and weighting methods. To increase the objectivity of developed index, the history of the damage indicators are excluded in the analysis. In addition, in order to ensure its reliability, results from index with and without consideration of the damage history were compared. Also, the index is also applied to real data for 2008 Gangwon region to verify its field applicability. Through the validation process this index confirmed the adequacy for the indicators selection and calculation method. The results of this study were analyzed based on the spatial and time vulnerability of the basin's water use, which can be applied to various parts such as priority decision-making for water business or policy, mitigations for the vulnerable components of the basin, and supporting measures to establishment by providing relevant information about it.